Network embedding is a general-purpose machine learning technique that encodes network
structure in vector spaces with tunable dimension. Choosing an appropriate embedding …

We present FastRP, a scalable and performant algorithm for learning distributed node
representations in a graph. FastRP is over 4,000 times faster than state-of-the-art methods …

Network embedding aims to seek low-dimensional vector representations for network
nodes, by preserving the network structure. The network embedding is typically represented …

Data embedding is used in many machine learning applications to create low-dimensional
feature representations, which preserves the structure of data points in their original space …

Network embedding methods aim at learning low-dimensional latent representation of
nodes in a network. These representations can be used as features for a wide range of tasks …

Networks are one of the most powerful structures for modeling problems in the real world.
Downstream machine learning tasks defined on networks have the potential to solve a …

Networks are a general language for describing complex systems of interacting entities. In
the real world, a network always contains massive nodes, edges and additional complex …

Network embedding aims to learn the low-dimensional representations of vertexes in a
network, while structure and inherent properties of the network is preserved. Existing …

Network embedding has been increasingly used in many network analytics applications to
generate low-dimensional vector representations, so that many off-the-shelf models can be …

This paper investigates the problem of network embedding, which aims at learning low-
dimensional vector representation of nodes in networks. Most existing network embedding …